|
|
--- |
|
|
library_name: transformers |
|
|
license: mit |
|
|
base_model: FacebookAI/xlm-roberta-large |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- precision |
|
|
- recall |
|
|
- f1 |
|
|
model-index: |
|
|
- name: xlm-large-finetuned-ner-covidmed-v5 |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
# xlm-large-finetuned-ner-covidmed-v5 |
|
|
|
|
|
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.0778 |
|
|
- Accuracy: 0.9818 |
|
|
- Precision: 0.9105 |
|
|
- Recall: 0.9395 |
|
|
- F1: 0.9241 |
|
|
- Age Precision: 0.9692 |
|
|
- Age Recall: 0.9725 |
|
|
- Age F1-score: 0.9708 |
|
|
- Date Precision: 0.9832 |
|
|
- Date Recall: 0.9927 |
|
|
- Date F1-score: 0.9880 |
|
|
- Gender Precision: 0.9539 |
|
|
- Gender Recall: 0.9848 |
|
|
- Gender F1-score: 0.9691 |
|
|
- Job Precision: 0.6667 |
|
|
- Job Recall: 0.8208 |
|
|
- Job F1-score: 0.7358 |
|
|
- Location Precision: 0.9394 |
|
|
- Location Recall: 0.9532 |
|
|
- Location F1-score: 0.9462 |
|
|
- Name Precision: 0.9128 |
|
|
- Name Recall: 0.9214 |
|
|
- Name F1-score: 0.9171 |
|
|
- Organization Precision: 0.8692 |
|
|
- Organization Recall: 0.8962 |
|
|
- Organization F1-score: 0.8825 |
|
|
- Patient Id Precision: 0.9786 |
|
|
- Patient Id Recall: 0.9796 |
|
|
- Patient Id F1-score: 0.9791 |
|
|
- Symptom And Disease Precision: 0.8632 |
|
|
- Symptom And Disease Recall: 0.8944 |
|
|
- Symptom And Disease F1-score: 0.8785 |
|
|
- Transportation Precision: 0.9692 |
|
|
- Transportation Recall: 0.9793 |
|
|
- Transportation F1-score: 0.9742 |
|
|
- Micro avg Precision: 0.9369 |
|
|
- Micro avg Recall: 0.9536 |
|
|
- Micro avg F1-score: 0.9452 |
|
|
- Macro avg Precision: 0.9105 |
|
|
- Macro avg Recall: 0.9395 |
|
|
- Macro avg F1-score: 0.9241 |
|
|
- Weighted avg Precision: 0.9381 |
|
|
- Weighted avg Recall: 0.9536 |
|
|
- Weighted avg F1-score: 0.9457 |
|
|
|
|
|
## Model description |
|
|
|
|
|
More information needed |
|
|
|
|
|
## Intended uses & limitations |
|
|
|
|
|
More information needed |
|
|
|
|
|
## Training and evaluation data |
|
|
|
|
|
More information needed |
|
|
|
|
|
## Training procedure |
|
|
|
|
|
### Training hyperparameters |
|
|
|
|
|
The following hyperparameters were used during training: |
|
|
- learning_rate: 2e-05 |
|
|
- train_batch_size: 32 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 42 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 4 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Age Precision | Age Recall | Age F1-score | Date Precision | Date Recall | Date F1-score | Gender Precision | Gender Recall | Gender F1-score | Job Precision | Job Recall | Job F1-score | Location Precision | Location Recall | Location F1-score | Name Precision | Name Recall | Name F1-score | Organization Precision | Organization Recall | Organization F1-score | Patient Id Precision | Patient Id Recall | Patient Id F1-score | Symptom And Disease Precision | Symptom And Disease Recall | Symptom And Disease F1-score | Transportation Precision | Transportation Recall | Transportation F1-score | Micro avg Precision | Micro avg Recall | Micro avg F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------------:|:----------:|:------------:|:--------------:|:-----------:|:-------------:|:----------------:|:-------------:|:---------------:|:-------------:|:----------:|:------------:|:------------------:|:---------------:|:-----------------:|:--------------:|:-----------:|:-------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:-----------------:|:-------------------:|:-----------------------------:|:--------------------------:|:----------------------------:|:------------------------:|:---------------------:|:-----------------------:|:-------------------:|:----------------:|:------------------:|:-------------------:|:----------------:|:------------------:|:----------------------:|:-------------------:|:---------------------:| |
|
|
| No log | 1.0 | 158 | 0.0963 | 0.9733 | 0.7903 | 0.9007 | 0.8387 | 0.8174 | 0.9845 | 0.8932 | 0.9814 | 0.9915 | 0.9865 | 0.7897 | 0.9913 | 0.8791 | 0.4495 | 0.7457 | 0.5609 | 0.9119 | 0.9302 | 0.9210 | 0.8216 | 0.8836 | 0.8515 | 0.7558 | 0.8911 | 0.8179 | 0.9286 | 0.9791 | 0.9531 | 0.7831 | 0.8741 | 0.8261 | 0.6636 | 0.7358 | 0.6978 | 0.8717 | 0.9371 | 0.9032 | 0.7903 | 0.9007 | 0.8387 | 0.8790 | 0.9371 | 0.9059 | |
|
|
| No log | 2.0 | 316 | 0.0762 | 0.9797 | 0.8970 | 0.9193 | 0.9078 | 0.9659 | 0.9725 | 0.9692 | 0.9791 | 0.9897 | 0.9844 | 0.9479 | 0.9848 | 0.9660 | 0.6392 | 0.7168 | 0.6757 | 0.9368 | 0.9514 | 0.9440 | 0.8627 | 0.9088 | 0.8851 | 0.8793 | 0.8885 | 0.8839 | 0.9752 | 0.9791 | 0.9771 | 0.8352 | 0.8477 | 0.8414 | 0.9485 | 0.9534 | 0.9509 | 0.9308 | 0.9451 | 0.9379 | 0.8970 | 0.9193 | 0.9078 | 0.9314 | 0.9451 | 0.9382 | |
|
|
| No log | 3.0 | 474 | 0.0761 | 0.9812 | 0.9018 | 0.9405 | 0.9199 | 0.9468 | 0.9794 | 0.9628 | 0.9844 | 0.9933 | 0.9889 | 0.9459 | 0.9848 | 0.9650 | 0.6606 | 0.8439 | 0.7411 | 0.9222 | 0.9525 | 0.9371 | 0.8981 | 0.9151 | 0.9065 | 0.8672 | 0.8807 | 0.8739 | 0.9729 | 0.9850 | 0.9789 | 0.8555 | 0.8908 | 0.8728 | 0.9643 | 0.9793 | 0.9717 | 0.9266 | 0.9536 | 0.9399 | 0.9018 | 0.9405 | 0.9199 | 0.9279 | 0.9536 | 0.9404 | |
|
|
| 0.1535 | 4.0 | 632 | 0.0778 | 0.9818 | 0.9105 | 0.9395 | 0.9241 | 0.9692 | 0.9725 | 0.9708 | 0.9832 | 0.9927 | 0.9880 | 0.9539 | 0.9848 | 0.9691 | 0.6667 | 0.8208 | 0.7358 | 0.9394 | 0.9532 | 0.9462 | 0.9128 | 0.9214 | 0.9171 | 0.8692 | 0.8962 | 0.8825 | 0.9786 | 0.9796 | 0.9791 | 0.8632 | 0.8944 | 0.8785 | 0.9692 | 0.9793 | 0.9742 | 0.9369 | 0.9536 | 0.9452 | 0.9105 | 0.9395 | 0.9241 | 0.9381 | 0.9536 | 0.9457 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.51.3 |
|
|
- Pytorch 2.6.0+cu124 |
|
|
- Datasets 3.6.0 |
|
|
- Tokenizers 0.21.1 |
|
|
|
